Canadian Census E&I – Lessons Learned from 2006 with Plans for 2011

Work Session on Statistical Data EditingVienna Austria, April 21-23 2008

Mike Bankier, Statistics Canada, bankier@statcan.ca

Outline of Talk

Changes Made for 2006 Census

Impact of adjusting occupancy status and imputation of total non-response households

Processing of demographic variables with an emphasis on age

Possible enhancements to E&I for 2011

Changes to 2006 Census

73% of dwellings mailed questionnaires18% of dwellings responded by Internet85% gave permission to link to tax formQuestionnaires captured using ICRNon-Response Follow-Up (NRFU) done from centralized officesFailed Edit Follow-Up (FEFU) done from call centres

2006 Census Changes

These new approaches reduced the field staff required by 46%

Because of widespread labour shortages in some regions, the collection period was extended from mid-July to the end of Aug. (Census day May15)

National NR rate 2.8% in 2006 vs 1.6% in 2001

Dwelling Classification Survey

Mistakes made in field classifying dwellings as occupied or unoccupied. Sample of dwellings revisited to reassess occupancy status for dwellings where no response receivedDCS estimated

17.4% of 934,564 dwelling classified as unoccupied were occupied and 29.1% of 366,527 dwellings classified as occupied but with no responses were actually unoccupied

Occupancy status for individual dwellings adjusted. Resulted in a 3.6% increase in the number of occupied dwellings and a 5.2% decrease in the number of unoccupied dwellings

Imputation of Total NR Households

After the DCS adjustment, total non-response dwellings had all responses imputed by borrowing unimputed responses from another householdUsing a single donor for total non-response was less likely to produce implausible results Weighting used in 2001 to convert unoccupied dwellings to occupied - it could transfer population from one city block to another and be noticed by users

Demographic E&I

Demographic E&I does minimum change imputation for blanks and inconsistencies so later program can form Census familiesAll demographic variables for all persons in household are imputed simultaneously using CANCEISThree types of Census families

Couples without childrenCouples with childrenLone Parents with children

Couple Editing Concepts

For a couple, they should beboth adults (age >=15) and

both married or both common-law and

have appropriate relationships to Person 1

Child/Parent Editing Concepts

For a child/parent pairAt least one parent must be 15 or more years older than the child and

A female parent must not be more than 50 years older than a child and

The relationships to Person 1 should be appropriate

0.85% In Wrong 5 Year Age Range - Data Capture Error

0

5,000

10,000

15,000

20,000

25,000

0 - 4

5 - 9

10 - 14

15 - 19

20 - 24

25 - 29

30 - 34

35 - 39

40 - 45

50 - 54

55 - 59

60 - 64

65 - 69

70 - 74

75 - 79

80 - 84

85 - 89

90 - 94

95 - 99

100 - 104

105 - 109

110 +

Captured Value

Correct Value

Analysis of Imputation of Age

AGEU and AGE represent respectively the age of the person before and after minimum change donor imputation

99.11% had AGEU = AGE

0.61% had AGEU = Blank/Invalid

0.28% had AGEU ≠ AGE because of an inconsistency between AGEU and another variable

AGE Imputation for WIFE

Female Lone Parent vs Child Ages Before Imputation

Female Lone Parent vs Child Ages After Imputation

WIFE vs Child Ages Before Imputation

WIFE vs Child Ages After Imputation

Number of Children by Age Difference With Mother

1

10

100

1,000

10,000

100,000

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Age Difference

Vital Statistics

Census Before Imputation

Census After Imputation

2011 Changes – Small Domains

Small domain (e.g. centenarians, same sex married couples) can have upwards bias because of response or data capture errors for persons outside the small domain

Sometimes no alternate source of data to verify the small domain count and the domain is too large to be manually reviewed 100%

2011 Changes – Small Domains

Manually review 20% sample of persons age 95+ to determine those with incorrect age

For other 80% of persons age 95+, use nearest neighbour imputation to determine those with incorrect age

Then in 2nd step, blank out incorrect ages and impute

2011 Changes – Use Failed Records as Donors

Sometimes stratum failure rate is so high that number of donors is insufficient

Failed records could be used as donors since frequently failed record is missing just one or two responses and would be suitable for imputing other responses

2011 Changes - More Minimum Change Donor Imputation

Will do more minimum change donor imputation and less deterministic imputation where possible

Will combine modules so more variables are imputed simultaneously where possible

Concluding Remarks

Sophisticated E&I programs can do a better job detecting and resolving edit failuresWith this comes the responsibility to make few assumptions regarding the characteristics of the non-respondents or those giving inconsistent responses The impact of imputation should be made clear to usersE&I should not be viewed as a panacea such that data quality standards can be lowered